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Seasonal Adjustment to Facilitate Forecasting: Empirical Results

William R. Bell (1) and Ekaterina Sotiris (2)

ABSTRACT:

In this paper we consider how well seasonal adjustment methods (X-11 and ARIMA model- based), and certain of their variations, satisfy one objective of seasonal adjustment: facilitating short-term forecasting of nonseasonal movements in time series. We do this via an empirical study using a number of seasonal time series of major U.S. economic aggregates. For these series we examine how forecast accuracy is affected by the following choices: (1) alternative choices of simple models for forecasting the seasonally adjusted series (or trend estimates); (2) use of seasonally adjusted series versus trend estimates; (3) use of time series of unrevised versus time series of revised seasonally adjusted data; (4) use of X-11 versus ARIMA model-based adjustment; and (5) use of seasonally adjusted data in forecasting versus directly forecasting the unadjusted series.

KEYWORDS:

X-11 seasonal adjustment, ARIMA model-based seasonal adjustment, seasonal adjustment revision, trend estimation





(1) William R. Bell is Senior Mathematical Statistician for Small Area Estimation, U.S. Census Bureau, 4600 Silver Hill Road, Washington, DC 20233. email : william.r.bell@census.gov

(2) Ekaterina Sotiris is a student at the University of Maryland and Statistician, U. S. Census Bureau email: ekaterina.sotiris@census.gov



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Source: U.S. Census Bureau | Center for Statistical Research and Methodology | (301) 763-1649 (or x12@census.gov) |  Last Revised: November 19, 2012